Update renovation.py

renovation.py

@@ -55,58 +55,58 @@ class RenovationQualityDataset(datasets.GeneratorBasedBuilder):
             task_templates=[ImageClassification(image_column="image", label_column="labels")],
         )
 
- def _split_generators(self, dl_manager):
-        # Download and extract images
-        image_paths = []
-        for label, url in _URLS.items():
-            extract_path = dl_manager.download_and_extract(url)
-            print(f"Extracted files for label {label} to path: {extract_path}")
-
-            # Get image paths
-            for root, _, files in os.walk(extract_path):
-                for file in files:
-                    if file.endswith(".jpeg"):  # Assuming all images are .jpeg
-                        image_paths.append((os.path.join(root, file), label))
-
-        print(f"Collected a total of {len(image_paths)} image paths.")
-
-        # Shuffle image paths
-        random.shuffle(image_paths)
-
-        # 80% for training, 10% for validation, 10% for testing
-        train_end = int(0.8 * len(image_paths))
-        val_end = int(0.9 * len(image_paths))
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "rows": image_paths[:train_end],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "rows": image_paths[train_end:val_end],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "rows": image_paths[val_end:],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, rows):
-        def file_to_image(file_path):
-            img = Image.open(file_path)
-            return np.array(img)
-
-        for id_, (image_file_path, label) in enumerate(rows):
-            image = file_to_image(image_file_path)
-            yield id_, {
-                'image_file_path': image_file_path,
-                'image': image,
-                'labels': label,
-            }
+     def _split_generators(self, dl_manager):
+            # Download and extract images
+            image_paths = []
+            for label, url in _URLS.items():
+                extract_path = dl_manager.download_and_extract(url)
+                print(f"Extracted files for label {label} to path: {extract_path}")
+    
+                # Get image paths
+                for root, _, files in os.walk(extract_path):
+                    for file in files:
+                        if file.endswith(".jpeg"):  # Assuming all images are .jpeg
+                            image_paths.append((os.path.join(root, file), label))
+    
+            print(f"Collected a total of {len(image_paths)} image paths.")
+    
+            # Shuffle image paths
+            random.shuffle(image_paths)
+    
+            # 80% for training, 10% for validation, 10% for testing
+            train_end = int(0.8 * len(image_paths))
+            val_end = int(0.9 * len(image_paths))
+    
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "rows": image_paths[:train_end],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "rows": image_paths[train_end:val_end],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "rows": image_paths[val_end:],
+                    },
+                ),
+            ]
+    
+        def _generate_examples(self, rows):
+            def file_to_image(file_path):
+                img = Image.open(file_path)
+                return np.array(img)
+    
+            for id_, (image_file_path, label) in enumerate(rows):
+                image = file_to_image(image_file_path)
+                yield id_, {
+                    'image_file_path': image_file_path,
+                    'image': image,
+                    'labels': label,
+                }